OpenRaiser/Intern-Atlas huggingface dataset:
Edge Computing Enabled by Unmanned Autonomous Vehicles
Venue: arXiv cs.DC 2020
Year: 2020

Pervasive applications are revolutionizing the perception that users have towards the environment. Indeed, pervasive applications perform resource intensive computations over large amounts of stream sensor data collected from multiple sources. This allows applications to provide richer and deep insights into the natural characteristics that govern everything that surrounds us. A key limitation of these applications is that they have high energy footprints, which in turn hampers the quality of experience of users. While cloud and edge computing solutions can be applied to alleviate the problem, these solutions are hard to adopt in existing architecture and far from become ubiquitous. Fortunately, cloudlets are becoming portable enough, such that they can be transported and integrated into any environment easily and dynamically. In this article, we investigate how cloudlets can be transported by unmanned autonomous vehicles (UAV)s to provide computation support on the edge. Based on our study, we develop GEESE, a novel UAVbased system that enables the dynamic deployment of an edge computing infrastructure through the cooperation of multiple UAVs carrying cloudlets. By using GEESE, we conduct rigorous experiments to analyze the effort to deliver cloudlets using aerial, ground, and underwater UAVs. Our results indicate that UAVs can work in a cooperative manner to enable edge computing in the wild.
Graph-based non-linear least squares optimization for visual place recognition in changing environments
Venue: arXiv cs.CV 2020
Year: 2020

Visual place recognition is an important subproblem of mobile robot localization. Since it is a special case of image retrieval, the basic source of information is the pairwise similarity of image descriptors. However, the embedding of the image retrieval problem in this robotic task provides additional structure that can be exploited, e.g. spatio-temporal consistency. Several algorithms exist to exploit this structure, e.g., sequence processing approaches or descriptor standardization approaches for changing environments. In this paper, we propose a graph-based framework to systematically exploit different types of additional structure and information. The graphical model is used to formulate a non-linear least squares problem that can be optimized with standard tools. Beyond sequences and standardization, we propose the usage of intra-set similarities within the database and/or the query image set as additional source of information. If available, our approach also allows to seamlessly integrate additional knowledge about poses of database images. We evaluate the system on a variety of standard place recognition datasets and demonstrate performance improvements for a large number of different configurations including different sources of information, different types of constraints, and online or offline place recognition setups.
Quality-Driven Dynamic VVC Frame Partitioning for Efficient Parallel Processing
Venue: arXiv cs.DC 2020
Year: 2020

VVC is the next generation video coding standard, offering coding capability beyond HEVC standard. The high computational complexity of the latest video coding standards requires high-level parallelism techniques, in order to achieve real-time and low latency encoding and decoding. HEVC and VVC include tile grid partitioning that allows to process simultaneously rectangular regions of a frame with independent threads. The tile grid may be further partitioned into a horizontal sub-grid of Rectangular Slices (RSs), increasing the partitioning flexibility. The dynamic Tile and Rectangular Slice (TRS) partitioning solution proposed in this paper benefits from this flexibility. The TRS partitioning is carried-out at the frame level, taking into account both spatial texture of the content and encoding times of previously encoded frames. The proposed solution searches the best partitioning configuration that minimizes the trade-off between multi-thread encoding time and encoding quality loss. Experiments prove that the proposed solution, compared to uniform TRS partitioning, significantly decreases multi-thread encoding time, with slightly better encoding quality.
Object sorting using faster R-CNN
Venue: arXiv cs.CV 2020
Year: 2020

In a factory production line, different industry parts need to be quickly differentiated and sorted for further process. Parts can be of different colors and shapes. It is tedious for humans to differentiate and sort these objects in appropriate categories. Automating this process would save more time and cost. In the automation process, choosing an appropriate model to detect and classify different objects based on specific features is more challenging. In this paper, three different neural network models are compared to the object sorting system. They are namely CNN, Fast R-CNN, and Faster R-CNN. These models are tested, and their performance is analyzed. Moreover, for the object sorting system, an Arduino-controlled 5 DoF (degree of freedom) robot arm is programmed to grab and drop symmetrical objects to the targeted zone. Objects are categorized into classes based on color, defective and non-defective objects.
Equipment Failure Analysis for Oil and Gas Industry with an Ensemble Predictive Model
Venue: arXiv cs.LG 2020
Year: 2020

This paper aims at improving the classification accuracy of a Support Vector Machine (SVM) classifier with Sequential Minimal Optimization (SMO) training algorithm in order to properly classify failure and normal instances from oil and gas equipment data. Recent applications of failure analysis have made use of the SVM technique without implementing SMO training algorithm, while in our study we show that the proposed solution can perform much better when using the SMO training algorithm. Furthermore, we implement the ensemble approach, which is a hybrid rule based and neural network classifier to improve the performance of the SVM classifier (with SMO training algorithm). The optimization study is as a result of the underperformance of the classifier when dealing with imbalanced dataset. The selected best performing classifiers are combined together with SVM classifier (with SMO training algorithm) by using the stacking ensemble method which is to create an efficient ensemble predictive model that can handle the issue of imbalanced data. The classification performance of this predictive model is considerably better than the SVM with and without SMO training algorithm and many other conventional classifiers.
Explanations of Machine Learning predictions: a mandatory step for its application to Operational Processes
Venue: arXiv cs.LG 2020
Year: 2020

In the global economy, credit companies play a central role in economic development, through their activity as money lenders. This important task comes with some drawbacks, mainly the risk of the debtors not being able to repay the provided credit. Therefore, Credit Risk Modelling (CRM), namely the evaluation of the probability that a debtor will not repay the due amount, plays a paramount role. Statistical approaches have been successfully exploited since long, becoming the most used methods for CRM. Recently, also machine and deep learning techniques have been applied to the CRM task, showing an important increase in prediction quality and performances. However, such techniques usually do not provide reliable explanations for the scores they come up with. As a consequence, many machine and deep learning techniques fail to comply with western countries' regulations such as, for example, GDPR. In this paper we suggest to use LIME (Local Interpretable Model-agnostic Explanations) technique to tackle the explainability problem in this field, we show its employment on a real credit-risk dataset and eventually discuss its soundness and the necessary improvements to guarantee its adoption and compliance with the task.
Web Routineness and Limits of Predictability: Investigating Demographic and Behavioral Differences Using Web Tracking Data
Venue: arXiv cs.CY 2020
Year: 2020

Understanding human activities and movements on the Web is not only important for computational social scientists but can also offer valuable guidance for the design of online systems for recommendations, caching, advertising, and personalization. In this work, we demonstrate that people tend to follow routines on the Web, and these repetitive patterns of web visits increase their browsing behavior's achievable predictability. We present an information-theoretic framework for measuring the uncertainty and theoretical limits of predictability of human mobility on the Web. We systematically assess the impact of different design decisions on the measurement. We apply the framework to a web tracking dataset of German internet users. Our empirical results highlight that individual's routines on the Web make their browsing behavior predictable to 85% on average, though the value varies across individuals. We observe that these differences in the users' predictabilities can be explained to some extent by their demographic and behavioral attributes.
A Memory Efficient Baseline for Open Domain Question Answering
Venue: arXiv cs.CL 2020
Year: 2020

Recently, retrieval systems based on dense representations have led to important improvements in open-domain question answering, and related tasks. While very effective, this approach is also memory intensive, as the dense vectors for the whole knowledge source need to be kept in memory. In this paper, we study how the memory footprint of dense retriever-reader systems can be reduced. We consider three strategies to reduce the index size: dimension reduction, vector quantization and passage filtering. We evaluate our approach on two question answering benchmarks: TriviaQA and NaturalQuestions, showing that it is possible to get competitive systems using less than 6Gb of memory.
MM-FSOD: Meta and metric integrated few-shot object detection
Venue: arXiv cs.CV 2020
Year: 2020

In the object detection task, CNN (Convolutional neural networks) models always need a large amount of annotated examples in the training process. To reduce the dependency of expensive annotations, few-shot object detection has become an increasing research focus. In this paper, we present an effective object detection framework (MM-FSOD) that integrates metric learning and meta-learning to tackle the few-shot object detection task. Our model is a class-agnostic detection model that can accurately recognize new categories, which are not appearing in training samples. Specifically, to fast learn the features of new categories without a fine-tuning process, we propose a meta-representation module (MR module) to learn intra-class mean prototypes. MR module is trained with a meta-learning method to obtain the ability to reconstruct high-level features. To further conduct similarity of features between support prototype with query RoIs features, we propose a Pearson metric module (PR module) which serves as a classifier. Compared to the previous commonly used metric method, cosine distance metric. PR module enables the model to align features into discriminative embedding space. We conduct extensive experiments on benchmark datasets FSOD, MS COCO, and PASCAL VOC to demonstrate the feasibility and efficiency of our model. Comparing with the previous method, MM-FSOD achieves state-of-the-art (SOTA) results.
Analysis of MiniJava Programs via Translation to ML
Venue: arXiv cs.PL 2020
Year: 2020

MiniJava is a subset of the object-oriented programming language Java. Standard ML is the canonical representative of the ML family of functional programming languages, which includes F# and OCaml. Different program analysis and verification tools and techniques have been developed for both Java-like and ML-like languages. Naturally, the tools developed for a particular language emphasise accurate treatment of language features commonly used in that language. In Java, this means objects with mutable properties and dynamic method dispatch. In ML, this means higher order functions and algebraic datatypes with pattern matching. We propose to translate programs from one language into the other and use the target language's tools for analysis and verification. By doing so, we hope to identify areas for improvement in the target language's tools and suggest techniques, perhaps as used in the source language's tools, that may guide their improvement. More generally, we hope to develop tools for reasoning about programs that are more resilient to changes in the style of code and representation of data. We begin our programme by outlining a translation from MiniJava to ML that uses only the core features of ML; in particular, it avoids the use of ML's mutable references.
What can a 1980s BASIC programming textbook teach us today?
Venue: arXiv cs.PL 2020
Year: 2020

Elementary Basic, published in 1982, is an introductory programming text with a novel central conceit, namely that the fictional 19th century detective Sherlock Holmes used a computer to help solve mysteries. It is also novel among similar books of its time for its focus on program design. In other regards, such as its use of the language BASIC, it is representative of its time. Over 35 years after it was written, I think it is worth looking back at it to see to what is still relevant today and what would be done differently. We may even learn something about teaching programming today. Of particular interest is the degree to which the use of BASIC influenced the content of the book.
A Review into Data Science and Its Approaches in Mechanical Engineering
Venue: arXiv cs.AI 2020
Year: 2020

Nowadays it is inevitable to use intelligent systems to improve the performance and optimization of different components of devices or factories. Furthermore, it's so essential to have appropriate predictions to make better decisions in businesses, medical studies, and engineering studies, etc. One of the newest and most widely used of these methods is a field called Data Science that all of the scientists, engineers, and factories need to learn and use in their careers. This article briefly introduced data science and reviewed its methods, especially it's usages in mechanical engineering and challenges and ways of developing data science in mechanical engineering. In the introduction, different definitions of data science and its background in technology reviewed. In the following, data science methodology which is the process that a data scientist needs to do in its works been discussed. Further, some researches in the mechanical engineering area that used data science methods in their studies, are reviewed. Eventually, it has been discussed according to the subjects that have been reviewed in the article, why it is necessary to use data science in mechanical engineering researches and projects.
Model-Based Visual Planning with Self-Supervised Functional Distances
Venue: arXiv cs.LG 2020
Year: 2020

A generalist robot must be able to complete a variety of tasks in its environment. One appealing way to specify each task is in terms of a goal observation. However, learning goal-reaching policies with reinforcement learning remains a challenging problem, particularly when hand-engineered reward functions are not available. Learned dynamics models are a promising approach for learning about the environment without rewards or task-directed data, but planning to reach goals with such a model requires a notion of functional similarity between observations and goal states. We present a self-supervised method for model-based visual goal reaching, which uses both a visual dynamics model as well as a dynamical distance function learned using model-free reinforcement learning. Our approach learns entirely using offline, unlabeled data, making it practical to scale to large and diverse datasets. In our experiments, we find that our method can successfully learn models that perform a variety of tasks at test-time, moving objects amid distractors with a simulated robotic arm and even learning to open and close a drawer using a real-world robot. In comparisons, we find that this approach substantially outperforms both model-free and model-based prior methods. Videos and visualizations are available here: http://sites.google.com/berkeley.edu/mbold.
Consolidating a Model for Describing Situated Software Practices
Venue: arXiv cs.SE 2020
Year: 2020

Many prescriptive approaches to developing software intensive systems have been advocated but each is based on assumptions about context. It has been found that practitioners do not follow prescribed methodologies, but rather select and adapt specific practices according to local needs. As researchers, we would like to be in a position to support such tailoring. However, at the present time we simply do not have sufficient evidence relating practice and context for this to be possible. We have long understood that a deeper understanding of situated software practices is crucial for progress in this area, and have been exploring this problem from a number of perspectives. In this position paper, we draw together the various aspects of our work into a holistic model and discuss the ways in which the model might be applied to support the long term goal of evidence-based decision support for practitioners. The contribution specific to this paper is a discussion on model evaluation, including a proof-of-concept demonstration of model utility. We map Kernel elements from the Essence system to our model and discuss gaps and limitations exposed in the Kernel. Finally, we overview our plans for further refining and evaluating the model.
SID: Incremental Learning for Anchor-Free Object Detection via Selective and Inter-Related Distillation
Venue: arXiv cs.CV 2020
Year: 2020

Incremental learning requires a model to continually learn new tasks from streaming data. However, traditional fine-tuning of a well-trained deep neural network on a new task will dramatically degrade performance on the old task -- a problem known as catastrophic forgetting. In this paper, we address this issue in the context of anchor-free object detection, which is a new trend in computer vision as it is simple, fast, and flexible. Simply adapting current incremental learning strategies fails on these anchor-free detectors due to lack of consideration of their specific model structures. To deal with the challenges of incremental learning on anchor-free object detectors, we propose a novel incremental learning paradigm called Selective and Inter-related Distillation (SID). In addition, a novel evaluation metric is proposed to better assess the performance of detectors under incremental learning conditions. By selective distilling at the proper locations and further transferring additional instance relation knowledge, our method demonstrates significant advantages on the benchmark datasets PASCAL VOC and COCO.
Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks
Venue: arXiv cs.CV 2020
Year: 2020

Recently deep learning-based image compression has shown the potential to outperform traditional codecs. However, most existing methods train multiple networks for multiple bit rates, which increase the implementation complexity. In this paper, we propose a new variable-rate image compression framework, which employs generalized octave convolutions (GoConv) and generalized octave transposed-convolutions (GoTConv) with built-in generalized divisive normalization (GDN) and inverse GDN (IGDN) layers. Novel GoConv- and GoTConv-based residual blocks are also developed in the encoder and decoder networks. Our scheme also uses a stochastic rounding-based scalar quantization. To further improve the performance, we encode the residual between the input and the reconstructed image from the decoder network as an enhancement layer. To enable a single model to operate with different bit rates and to learn multi-rate image features, a new objective function is introduced. Experimental results show that the proposed framework trained with variable-rate objective function outperforms the standard codecs such as H.265/HEVC-based BPG and state-of-the-art learning-based variable-rate methods.
CLEAR: Contrastive Learning for Sentence Representation
Venue: arXiv cs.CL 2020
Year: 2020

Pre-trained language models have proven their unique powers in capturing implicit language features. However, most pre-training approaches focus on the word-level training objective, while sentence-level objectives are rarely studied. In this paper, we propose Contrastive LEArning for sentence Representation (CLEAR), which employs multiple sentence-level augmentation strategies in order to learn a noise-invariant sentence representation. These augmentations include word and span deletion, reordering, and substitution. Furthermore, we investigate the key reasons that make contrastive learning effective through numerous experiments. We observe that different sentence augmentations during pre-training lead to different performance improvements on various downstream tasks. Our approach is shown to outperform multiple existing methods on both SentEval and GLUE benchmarks.
Continual Learning in Task-Oriented Dialogue Systems
Venue: arXiv cs.CL 2020
Year: 2020

Continual learning in task-oriented dialogue systems can allow us to add new domains and functionalities through time without incurring the high cost of a whole system retraining. In this paper, we propose a continual learning benchmark for task-oriented dialogue systems with 37 domains to be learned continuously in four settings, such as intent recognition, state tracking, natural language generation, and end-to-end. Moreover, we implement and compare multiple existing continual learning baselines, and we propose a simple yet effective architectural method based on residual adapters. Our experiments demonstrate that the proposed architectural method and a simple replay-based strategy perform comparably well but they both achieve inferior performance to the multi-task learning baseline, in where all the data are shown at once, showing that continual learning in task-oriented dialogue systems is a challenging task. Furthermore, we reveal several trade-offs between different continual learning methods in term of parameter usage and memory size, which are important in the design of a task-oriented dialogue system. The proposed benchmark is released together with several baselines to promote more research in this direction.
Autocalibration Subsystem for Cable-Driven Parallel Robots
Venue: arXiv cs.RO 2020
Year: 2020

For Cable-Driven Parallel Robots, the cable length is the only element we control. Better knowledge of this parameter will improve robot performances. We propose a new cable length autocalibration subsystem. It consists of an instrumented cable, inductive sensors and a controller for digital data processing. Cable would be instrumented by metallic marks, with variation in distance between successive marks. Inductive sensors will allow the detection of metallic marks; it will be placed vertically on the support, with variation in distance between successive sensors. Each detection will correspond to a cable length. By analyzing the succession of cable lengths detected at particular moments, we can identify our position on the cable, then deduce its length. A closed-loop autocalibration may allow continuous cable length adjustment.